1. Field of the Invention
The present invention relates to a device for measuring a vectorial physical quantity.
The present invention further relates to a method for measuring a vectorial physical quantity.
2. Related Art
Nowadays GPS navigation facilities are available that can relatively accurately determine a position of a vehicle. However, in some circumstances alternative navigation methods are required as GPS-navigation signals are not always available, for example at locations below sea level and in buildings. One such alternative method is based on data obtained from inertial sensors. Inertial sensors comprise gyroscopes and accelerometers. Gyroscopes provide information about the angular rate of the vehicle from which the orientation of the vehicle can be derived and accelerometers provide information about its acceleration. Such sensors are typically available in a set, wherein they are mutually orthogonally arranged. For example a 2D or a 3D-gyroscope-set comprises respectively two and three mutually orthogonally arranged gyroscopes. If the initial position, velocity and orientation of a vehicle are known, its momentaneous velocity and position can be estimated by numerical integration of the acceleration and orientation data obtained from the accelerometers and gyroscopes. Generally accelerometers and gyroscopes have a systematic error, also denoted as bias, resulting in a drift in position indication, exponential in time. Accordingly, such navigation systems based on inertial sensors need to be calibrated periodically to measure and compensate the sensor biases. With low-cost sensors, and without bias compensation, the navigation solution becomes useless within minutes.
The Allan Variance (AVAR) is a well-known method for analyzing a time sequence to determine the intrinsic noise in a system as a function of the averaging time. Stockwell, “Bias Stability Measurement: Allan Variance” www.xbow.com/pdf/Bias_Stability_Measurement.pdf, applies this method to inertial sensors and points out that the Allan Variance is mainly determined by two factors. At short averaging times, the Allan Variance is dominated by the white (gaussian) noise in the sensor. There is a direct correlation between the standard deviation of the white noise contribution of the output vs. time with the slope of the Allan Variance at small t. For gyroscopes this is also referred to as angle random walk (ARW). However for relatively long integration times, the Allan Variance starts to increase again. This is due to so called 1/fα low frequent correlated noise in the sensor, inherent instability in the output of the sensor and also referred as rate random walk (RRW) in the case of gyroscopes. As a result of these two contributions the Allan variance has a minimum. It is noted in the cited article that the minimum variance is the best stability that can be achieved with a fully modeled sensor and active bias estimation. In the sequel the integration time for which the minimum of the Allan variance is obtained will be denoted herein also as the Allan minimum time.
It is noted that test procedures for inertial sensors are described in “IEEE Recommended Practice for Inertial Sensor Test Equipment. Instrumentation, Data Acquisition, and Analysis”; IEEE Std 1554-2005 ED.
There is a need to improve an accuracy with which a vectorial physical quantity such as an acceleration, a rotation or a magnetic field strength can be measured.